Biology subjects

Croft, S.

Publications and source records attributed to Croft, S..

6 recordsLinked to original sources

bioRxiv2026

Temporal variation in demography of temperate bats: consequences for population dynamics and disease

Bat populations experience inter-annual variation in demographic rates in response to environmental conditions. This variation has the potential to impact population sizes and structures, in addition to population-level processes such as disease spread. To establish the influence of variation in demography on these processes, we develop a spatial, individual based model of a serotine bat (Eptesicus serotinus) population, within which we introduce a synthetic lyssavirus-like disease. Model results show that increasing demographic variation, particularly in survival rates, may drive substantial population decline in bat populations. Increasing environmental fluctuations driven by climate change may therefore be problematic for population persistence. The likelihood of disease persistence was also reduced by increasing variation. These findings highlight the limitations of only considering mean demographic rates for prediction of population size change and disease dynamics from models.

ecology↗

bioRxiv2025

Predicting West Nile Virus risk across Europe for the current and future conditions

Vector-borne diseases have significant impacts on animal and human health globally, and these impacts are likely to increase in the future due to environmental and climate change. Understanding where to target surveillance and control measures to mitigate the impacts of emerging vector-borne diseases can be challenging when pathogens or disease is absent. In this study, we utilise a species distribution modelling approach previously applied to the UK to predict areas at higher risk of mosquito-borne disease across Europe, using West Nile Virus (WNV) as a case study. WNV is an Orthoflavivirus that is naturally transmitted between Culex mosquitoes and a range of avian species. However, it can spread to hosts such as humans and horses where it has the potential to lead to severe illness and mortality. Suitability predictions for Culex (Cx. pipiens and Cx. modestus) and avian hosts (mainly Passerine species) are made across Europe to determine potential risk of WNV circulation and establishment. These maps are then combined with information on human and horse density to determine risk to human and equine health. The resulting risk maps reveal that across Europe, there are areas of higher and lower risk that are predominantly driven by vector suitability as avian hosts are widespread. These predictions are projected into the future in 2100 using best- and worst-case Shared Socioeconomic Pathways (SSP1 and SSP5 respectively) to determine how risk may change over time, revealing that some areas see an increase in suitability for both vectors and hosts leading to higher risk (e.g., central England and northern Belgium) whilst other areas see a decline in suitability and consequently lower WNV risk (e.g., northern Italy and western Germany). Overall, this work will improve understanding of mosquito-borne disease risk in changing environments and demonstrates how species distribution modelling can be used to aid contingency preparedness by highlighting areas at higher risk of emerging disease.

pathology↗

bioRxiv2025

Predicting the distribution of common wild mammal species across Europe - are there sufficient occurrence data?

ContextKnowledge of where wildlife species are and in what number is critical to support robust contingency planning for diseases affecting animal and human health. For common widespread species in particular, comprehensive surveillance is impractical and challenging to coordinate. This is where models can be useful, to fill in the gaps and direct survey efforts to maximise understanding. The ENETWILD project was commissioned by EFSA in 2017 to review available data and modelling methods to predict distributions for key species involved in diseases of concern (e.g., wild boar and African swine fever). Here, we outline the latest methodology to predict species distributions based on occurrence data and outline the areas where further data would be most helpful. MethodWe present a generic framework to model the distribution of common mammal species in Europe, using global data. Based on occurrence records from large scale repositories, our method first attempts to address known biases to produce a presence-absence dataset and then applies random forest modelling to predict habitat suitability. We apply this approach to twelve species spanning distinct survey methodologies within mammal recording (visual observation, trapping, and bats/audio monitoring). ResultsModel performance was acceptable across all species within the limits of the available training data (AUC close to or above 0.7). However, beyond these limits the reliability of prediction was substantially reduced. Assessment of the available data suggested large areas of Eastern and Southern (particularly mountainous) parts of Europe have a distinct environmental signature not sufficiently captured by the existing sampling. ConclusionsWithin Europe there remain environmental conditions which are not well represented by existing surveillance, predominantly in Eastern and Southern regions. To reliably apply a robust modelling approach to a Europe-wide context targeted data collection is required.

ecology↗

bioRxiv2024

Using correlative and mechanistic species distribution models to predict vector-borne disease risk for the current and future environmental and climatic change: a case study of West Nile Virus in the UK.

Globally, vector-borne diseases have significant impacts on both animal and human health, and these are predicted to increase with the effects of climate change. Understanding the drivers of such diseases can help inform surveillance and control measures to minimise risks both now and in the future. In this study, we illustrate a generalised approach for assessing disease risk combining species distribution models of vector and wildlife hosts with data on livestock and human populations using the potential emergence of West Nile Virus (WNV) in the UK as a case study. Currently absent in the UK, WNV is an orthoflavivirus with a natural transmission cycle between Culex mosquitos (Cx. pipiens and Cx. modestus) and birds. It can spread into non-target hosts (e.g., equids, humans) via mosquito bites where it can cause febrile disease with encephalitis and mortality in severe cases. We compared six correlative species distribution models and selected the most appropriate for each vector based on a selection of performance measures and compared this to mechanistic species distribution models and known distributions. We then combined these with correlative species distribution models of representative avian hosts, equines, and human population data to predict risk of WNV occurrence. Our findings highlighted areas at greater risk of WNV due to higher habitat suitability for both avian hosts and vectors, and considered how this risk could change by 2100 under a best-case Shared Socioeconomic Pathway (SSP1) and worst-case (SSP5) future climate scenario. Generally, WNV risk in the future was found to increase in south-eastern UK and decrease further north. Overall, this paper presents how current and future vector distributions can be modelled and combined with projected host distributions to predict areas at greater risk of novel diseases. This is important for policy decision making and contingency preparedness to enable adaptation to changing environments and the resulting shifts in vector-borne diseases that are predicted to occur.

ecology↗

bioRxiv2024

Selection of movement rules to simulate species dispersal in a mosaic landscape model

Dispersal is an ecological process central to population dynamics, representing an important driver of movement between populations and across landscapes. In spatial population models for terrestrial vertebrates, capturing plausible dispersal behaviour is of particular importance when considering the spread of disease or invasive species. The distribution of distances travelled by dispersers, or the dispersal kernel, is typically highly skewed, with most individuals remaining close to their origin but some travelling substantially further. Using mechanistic models to simulate individual dispersal behaviour, the dispersal kernel can be generated as an emergent property. Through stepwise simulation of the entire movement path, models can also account for the influence of the local environment, and contacts during the dispersal event which may spread disease. In this study, we explore a range of simple rules to emulate individual dispersal behaviour within a mosaic model generated using irregular geometry. Movement rules illustrate a limited range of behavioural assumptions and when applied across these simple synthetic landscapes generated a wide range of emergent kernels. Given the variability in dispersal distances observed within species, our results highlight the importance of considering landscape heterogeneity and individual-level variation in movement, with simpler rules approximating random walks providing less plausible emergent kernels. As a case study, we demonstrate how rule sets can be selected by comparison to an empirical kernel for a study species (red fox; Vulpes vulpes). These results provide a foundation for the selection of movement rules to represent dispersal in spatial agent-based models, however, we also emphasise the need to corroborate rules against the behaviour of specific species and within chosen landscapes to avoid the potential for these rules to bias predictions.

ecology↗

bioRxiv2019

Structuring the unstructured: estimating species-specific absence from multi-species presence data to inform pseudo-absence selection in species distribution models

O_LISpecies distribution models (SDMs) are an increasingly popular tool in ecology which, together with a vast wealth of data from citizen science projects, have the potential to dramatically improve our understanding of species behaviour for applications such as conservation and wildlife management. However, many of the best performing models require information regarding survey effort, specifically absence, which is typically lacking in opportunistic datasets. To facilitate the use of such models, pseudo-absences from locations without recorded presence must be assumed. Several studies have suggested that survey effort, and hence likely absence, could be estimated from presence-only data by considering records across \"target groups\" of species defined according to taxonomy.\nC_LIO_LIWe performed a probabilistic analysis, computing the conditional probability of recording a species given a particular set of species are also recorded, to test the validity of defining target groups by taxonomic order and to explore other potential groupings. Based on this quantification of recording associations we outline a new method to inform pseudo-absence selection comparing predictive performance, measured the area under curve (AUC) statistic, against the standard method of selection across a series of SDMs.\nC_LIO_LIOur findings show some support for target grouping classification based on taxonomy but indicate that an alternative classification using survey method may be more appropriate for informing effort and consequently absence. Across 49 terrestrial mammal species, pseudo-absence selection using our proposed method outperformed that of the standard method showing an improvement in the predictive performance of presence-absence models for 17 out of 22 with sufficient data to elicit a significant difference. Based on our method we also observed a substantial improvement in the performance of presence-absence models compared to that of presence-only models (MaxEnt) with a higher AUC for all 22 species showing a significant difference between approaches.\nC_LIO_LIWe conclude that our method produces sensible robust pseudo-absences which either compliment patterns in known presences or, where conflicts occur, are explainable in terms of ecological variables potentially improving our understanding of species behaviour. Furthermore, we suggest that presence-absence models using these pseudo-absences provide a viable alternative to MaxEnt when modelling using presence-only data.\nC_LI

ecology↗